67 citations · 67 across the 13 of their papers we have counts for
12 papers · 1 filter
REVEAL: Reverse Engineering Agentic Latents from Frontier-Model Traces
Yuetian Du, Meng Wang, Yucheng Wang +9
Frontier LLM agents perform complex interactive tasks, but their inference costs motivate transferring their experience to smaller open-source agents. Distillation and latent commu…
The Illusion of : Evaluating the Breakdown of Counterfactual Reasoning in LLMs
Yucheng Wang, Yuetian Du, Zhengyi Liu +8
Counterfactual reasoning requires models to reason beyond the observed world and explain how altered conditions propagate through downstream consequences. Existing benchmarks large…
MetaRAG: Belief-Action Aligned Policy Optimization for Agentic RAG
Qiuyi Qi, Tian Liang, Jiamu Wang +7
Agentic retrieval-augmented generation (RAG) requires language models to decide when to continue searching and when to answer. Existing RL-based methods rely on external supervisio…
NGS-Marker: Robust Native Watermarking for 3D Gaussian Splatting
Hao Qin, Yukai Sun, Luyuan Chen +5
With the rapid development and adoption of 3D Gaussian Splatting (3DGS), the need for effective copyright protection has become increasingly critical. Existing watermarking techniq…
D2R2: Discrete Diffusion with Regulation Reinforcement for Single-Cell Perturbation Prediction
Ninghan Fan, Qi Liu, Xunuo Zhu +9
Predicting single-cell transcriptomic responses to genetic perturbations is central to functional genomics and virtual-cell modeling. Existing approaches, however, typically predic…
CARE: Confidence-Aware Reasoning for Reliable Medical VQA
Yuetian Du, Yucheng Wang, Zhenyuan Chen +9
Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produce Chain-of-Thought (CoT) reasoning for visual question answering, yet these mo…
Living-Harness Is an Interactive-Agent Evolver
Yuetian Du, Yucheng Wang, He Xu +9
Large language model (LLM) agents may recover from a failure within an episode or after a retry, yet the same execution failure can recur in later tasks because post-episode feedba…
STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training
Qiuyi Qi, Tian Liang, Mutian Bao +8
Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed rewards often lead to traject…
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs
Qiuyi Qi, Jinjian Zhang, Mutian Bao +9
Despite their strong reasoning capabilities and extensive world knowledge, Large Language Models (LLMs) frequently generate plans that violate task constraints, undermining their r…
Confidence Calibration for Multimodal LLMs: An Empirical Study through Medical VQA
Yuetian Du, Yucheng Wang, Ming Kong +4
Multimodal Large Language Models (MLLMs) show great potential in medical tasks, but their elicited confidence often misaligns with actual accuracy, potentially leading to misdiagno…
Variation-aware Flexible 3D Gaussian Editing
Hao Qin, Yukai Sun, Meng Wang +3
Indirect editing methods for 3D Gaussian Splatting (3DGS) have recently witnessed significant advancements. These approaches operate by first applying edits in the rendered 2D spac…
Linking Perception, Confidence and Accuracy in MLLMs
Yuetian Du, Yucheng Wang, Rongyu Zhang +5
Recent advances in Multi-modal Large Language Models (MLLMs) have predominantly focused on enhancing visual perception to improve accuracy. However, a critical question remains une…